Can machine learning aid in delivering new use cases and scenarios in 5G?

Buda, Teodora Sandra and Assem, Haytham and Xu, Lei and Raz, Danny and Margolin, Udi and Rosensweig, Elisha and Lopez, Diego R. and Corici, Marius Iulian and Smirnov, Mikhail and Mullins, Robert and Uryupina, Olga and Mozo, Alberto and Ordozgoiti, Bruno and Martin, Angel and Alloush, Alaa and O'Sullivan, Pat and Ben Yahia, Imen Grida (2016) Can machine learning aid in delivering new use cases and scenarios in 5G? In: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium :. Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium . Institute of Electrical and Electronics Engineers Inc., TUR, pp. 1279-1284. ISBN 9781509002238

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Abstract

5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.

Item Type: Book Section
Additional Information: Publisher Copyright: © 2016 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:18
Last Modified: 30 Jul 2023 16:05
URI: http://repository-testing.wit.ie/id/eprint/5198

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